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Do Hedge Funds Trade on Private Information? Evidence from

Upcoming Changes in Analysts’ Stock Recommendations

April Klein

Stern School of Business

New York University and Warwick Business School

Anthony Saunders

Stern School of Business

New York University

Yu Ting Forester Wong

Columbia Business School

Columbia University

Abstract1

Two large financial institutions recently settled allegations of selective disclosure of

private information about upcoming stock recommendation changes from supply-side

analysts to hedge fund clients. This paper explores two research questions: Is the transfer

of private information widespread? Do large hedge funds trade advantageously on

upcoming recommendation changes? We find results consistent with large hedge funds

trading profitably up to two days prior to analysts’ recommendation changes. Further,

individual hedge funds tend to “anticipate” recommendation changes from a small

number of brokers only, suggesting a favored hedge fund-brokerage house relationship.

We find no similar trading patterns for other financial institutions.

1 We thank Cindy Alexander, Dan Amiram, Jennifer Arlen, Robert Bloomfield, Edwige Cheynel, Christine

Cuny, Fabrizio Ferri, Edward Glickman, Trevor Harris, Colleen Honigsberg, Alon Kalay, Sharon Katz,

Urooj Khan, Paul Mahoney, Nahum Melumad, Suresh Nallareddy, Doron Nissim, Stephen Penman,

Miguel Rivas, Gil Sadka, Ron Shalev, Jonathan Sokobin, Richard Taffler, Julian Yao, and seminar

participants at Columbia University, NYU Law School, the University of Kentucky, University of Virginia

Law School, and Warwick Business School for helpful comments and suggestions.

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“We believe that the practice of selective disclosure leads to a loss of investor confidence

in the integrity of our capital markets… Investors lose confidence in the fairness of the

markets when they know that other participants may exploit ‘unerodable informational

advantages’ derived not from hard work or insights, but from their access to corporate

insiders”

Final Rule: Selective Disclosure and Insider Trading (SEC, 2000)

1. Introduction

In December 2002, the SEC, along with the New York Stock Exchange (NYSE),

the NASD, the New York Attorney General’s Office (NYAGO), and the North American

Administrators Association announced a “Global Analyst Research Settlement” with ten

large Wall Street firms (SEC 2002b). The 2003 Settlement Agreement put in place new

structures in brokerage firms’ equity research departments intended to “protect the small

investor and restore integrity to the marketplace” (SEC 2002b).

Despite the emphasis placed in the SEC releases on restoring “integrity” to the

marketplace, the 2003 Settlement Agreement did not explicitly prohibit the selective

disclosure of analysts’ recommendations to their investor clients prior to their public

release. Whereas there might be market efficiency reasons for allowing analysts to

provide early disclosures of recommendation changes to large institutions, this behavior

is contrary to the SEC’s position on the selective dissemination of private, market-

moving information. For example, Regulation FD, adopted in October 2000, specifically

proscribes issuers (publicly-traded companies) from selectively disclosing material

nonpublic information to investors. As the quotes above from that final rule illustrate, the

SEC’s rationale for instituting Regulation FD is to level the information playing field

among all investors.

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In this paper, we examine whether there is evidence consistent with large hedge

funds trading opportunistically on the leakage of future sell-side analysts’ stock

recommendation changes since the institution of the 2003 Settlement Agreement. We

choose large hedge funds because we believe they are the most likely type of investor to

seek out and trade on private information. Our assertion is based, partly, on the following

observations. First, the two-and-twenty compensation structure of hedge funds is

consistent with hedge funds wanting to gain an informational advantage over other

traders. Other institutional investors, for example, mutual funds, banks and insurance

companies have different compensation structures, ones not too closely aligned with

short-run profits.2 Second, hedge funds are, for the most part, unregulated and therefore,

can fly under the radar screen in enacting their trading strategies. Third, as we explain

more fully below, sell-side analysts of large brokerage firms are incentivized to curry

favor with large hedge funds due to how their compensation packages are structured and

through increased trading commissions earned by the brokerage firm. Fourth, there are

both anecdotal and large-scale finance studies consistent with hedge funds trading

advantageously on other private information, for example, advanced notification of other

investors’ trading orders (Lewis, 2013; Caruthers, 2014), direct news feeds (NYAGO,

2014) and upcoming mergers and acquisitions (Morgenson, 2012).

Fifth, and most germane to this study, there have been two recent legal

settlements concerning large brokerage house sell-side analysts providing private

information to large hedge funds. In 2013, Citigroup Global Markets Inc. agreed to pay

$30 million to the Massachusetts Securities Division (MSD) to settle charges that one of

2 In 2013, for example, the top 25 compensated hedge fund managers earned $21.5 billion in total

compensation (Institutional Investor’s Alpha, 2014).

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its analysts, in violation of the firm’s rules and regulations, shared private research with

four of its large clients one day in advance of his published research report on Apple’s

demand for iPhones (MSD, 2013). According to the Consent Decree (MSD, 2013), three

of these institutions traded on this information prior to its public disclosure.3 In 2014,

BlackRock reached a settlement with the New York attorney general’s office to end its

“global analyst survey program” aimed at “front running” changes in sell-side analysts’

recommendations (OAGNY, 2014; Morgenson, 2012). According to Morgenson (2012),

hedge funds exploited their access to this private information by buying upgrades and

selling downgrades prior to the public release of these recommendation changes.

(Appendix A contains a fuller discussion of the legal and regulatory environment

surrounding the selective disclosure of material information).

Our main research question is: are these isolated cases, or, conversely, has a larger

group of hedge funds traded prior to analysts’ recommendation changes? To answer this

question, we gather analysts’ recommendation changes from 2005 through 2011,

inclusive, for the seven large brokerage firms identified by Morgenson (2012). Using

SEC Form 13F filings, we select all 57 hedge funds managing investments of at least $10

billion, and then examine their trading patterns prior to and after the public disclosure of

these recommendation changes.

We are able to identify the exact date of the recommendation change from the

FirstCall database. Hedge fund trading dates, however, are not available because hedge

funds are required to report their holdings on a quarterly basis only through a Form 13F

3 According to the Consent Decree, Citigroup Global Markets privately and selectively disclosed the Apple

demand information on Thursday, December 13, 2012; a public report on the demand information was

distributed on Friday, December 14, 2012, and a Citigroup Research Report on Apple containing a

downgrade from a “buy” recommendation to a “neutral” recommendation was published on Sunday,

December 16, 2012. The trades by the institutions were on December 13 and 14, 2012.

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filing. Thus, in any quarter, we can observe a change in holdings for that quarter, but we

are unable to identify the timing of the trades.

To overcome this data limitation, we use an identification scheme in which we

line up all recommendation changes by the trading days following the Form 13F end

date. For example, suppose the Form 13F end date is March 31, then Day +1 would be

the first trading day after March 31; Day +2 would be the second trading day after March

31; and so on. For Form 13Fs with a June 30th

end date, we would have the same

classification scheme. We then create portfolios of recommendation changes by these

trading days, i.e., for Day +1, Day +2, Day +3, etc.

If hedge funds trade on private information about analysts’ recommendation

changes, then we expect the timing of these trades to be relatively close to the

recommendation issuance date (see Irvine, Lipson, and Puckett, 2007; Kadan, Michaely,

and Moulton, 2013). We therefore restrict our portfolios of recommendation changes to

those occurring within 10 days subsequent to the Form 13F end date. By doing this, we

believe that changes in quarter t-1 holdings for these stocks most likely capture the

trading activity related to any transfer of private information.

Our empirical results strongly support the view that hedge funds trade

opportunistically prior to the disclosure of recommendation changes. First, we document

a positive association between changes in hedge fund holdings in quarter t-1 and changes

in analysts’ recommendations for Days +1 and +2. Specifically, we find that Day +1

upgrades are preceded by an increase in holdings in the upgraded stock, and Day +1 and

+2 downgrades are preceded by a decrease in holdings in the downgraded stock. We see

no significant associations between changes in holdings in quarter t-1 and stock

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recommendation changes for Days +3 through +10. These results are consistent with

hedge funds trading one to two days prior to a recommendation change.

Second, we find that changes in holdings in quarter t-1 for recommendation

changes made on Days +1 or +2 are temporary. Following recommendation upgrades,

21% of purchases are reversed completely in quarter t; following analyst downgrades,

13% of sales are reversed completely in quarter t. In contrast, reversals for purchases and

sales unaccompanied by recommendation changes are 0.2% and 1.5%, respectively.

These results suggest that hedge funds have shorter investment horizons for stocks

bought or sold prior to recommendation changes than for other stocks held in their

portfolios.

Third, we present evidence of a favored relationship between one hedge fund and

one or two brokerage houses only. For each hedge fund, we calculate its dollar trades

prior to each brokerage house’s recommendation changes. If there is no favored

relationship, then we should observe a random “anticipation” of recommendation

changes across the seven brokerage houses. We do not find this. Instead, we find that

each hedge fund trades more actively on upcoming recommendation changes made by

one or two brokerage houses only. This finding is consistent with the proposition that

any leakage of information occurs between one (or two) brokerage house(s) and his/her

preferred clients.

Fourth, we find that hedge fund trades are profitable only when accompanied by a

future recommendation change. When hedge funds trade in stocks with recommendation

changes, they earn an average annualized abnormal return of 9.96% for upgrades, or

avoid an average annualized abnormal return of -11.28%. In contrast other hedge fund

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trades earn abnormal returns not significantly different from zero. If hedge fund

managers had superior forecasting ability, then we should notice positive abnormal

returns across all securities in their portfolios.

We also examine trading patterns prior to upgrades and downgrades for other

large institutional traders – namely 146 large banks, 27 insurance companies and 319

large growth-oriented mutual funds. We find no evidence that these institutions trade

prior to analysts’ recommendation changes. Nor do we observe any significant reversals

in positions for upgrades and downgrades that were found for the group of large hedge

funds. We therefore conclude that the opportunistic trading patterns surrounding

analysts’ recommendation changes are limited primarily to large hedge funds, and cannot

be extended to other large institutional traders.

An alternative explanation to our hedge fund trading patterns is that the causality

could be reversed — that is, analysts may change their recommendations after observing

abnormal levels of hedge fund trading imbalances. While we cannot absolutely rule out

this possibility, we perform several tests of reverse causality to see if the data conform to

this alternative hypothesis. Our empirical tests are not consistent with a reverse causality

explanation. Thus, we infer that the flow of information is not from hedge fund to

analyst, but the other way around.

Our findings complement and extend prior studies on whether institutions trade

opportunistically prior to public disclosures of recommendation changes. Two papers

provide evidence of abnormal trading prior to the 2003 Settlement Agreement. Irvine, et

al. (2007), using trading data from March 31, 1996, to December 31, 1997 and from

March 31, 2000, to December 31, 2000, report abnormal aggregate trading volume by

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their sample of institutions prior to “strong buy” and “buy” recommendation initiations.

In a similar, but more general vein, Christophe, Ferri, and Hsieh (2010) report abnormal

short selling interests for the subset of firms in which short-selling data are available

(September 13, 2000 through July 10, 2001) prior to public announcements of

downgrades.

Two papers utilize data encircling the pre- and post 2003 Settlement Agreement

date. Kadan et al. (2013) present evidence of differences in trading behavior between all

institutions and individual investors around recommendation changes. However, they

present mixed evidence on whether institutions in fact trade prior to upgrades and

downgrades. In this study, we separate institutions into large hedge funds, banks,

insurance companies, and growth-oriented mutual funds and find evidence that only

hedge funds trade prior to recommendation changes. Juergens and Lindsey (2009) find

that NASDAQ market makers trade up to two days early on downgrades made by

analysts in the same brokerage firm, whereas unaffiliated market makers do not trade

early. They find no abnormal trading prior to upgrades, however. Their findings suggest

a transfer of private information within the investment bank itself for downgrades.

Our findings also contribute to an important and growing literature investigating

different avenues in which hedge funds obtain material non-public information and trade

on it. Massoud, Nandy, Saunders, and Song (2011) and Ivashina and Sun (2011) provide

evidence on hedge funds’ access to private information from syndicated loans.

Morgenson (2012) provides anecdotal evidence of hedge funds trading on information

obtained from their involvement in mergers and acquisition deals. In these papers, the

source of the private information is from the hedge fund itself; whereas in this study, the

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apparent source is from a third party − sell-side analysts working for large brokerage

firms.

The large brokerage houses have written internal policies specifically prohibiting

the selective disclosure of research reports to their clients (MSD, 2013; OAGNY, 2014).

One obvious question is why analysts would supply this information to their hedge fund

clients in violation of this policy. We proffer two possible explanations. The first is

related to how analysts are compensated by their investment banking houses. In 2002, the

SEC, the National Association of Security Dealers (NASD) and the NYSE passed rules

prohibiting investment banks from compensating their analysts through their services to

the investment bank. In 2003, the NASD and the NYSE amended these rules by

regulating the type of inputs that investment banks must use when compensating their

research analysts. One factor that banks must use is the ratings (relative assessment

across all analysts in the same industry) that their analysts receive from their clients.

Thus, analysts’ compensation depends directly on the usefulness that their services

provide to their hedge fund clients, with the most “useful” analyst receiving the highest

rating. We believe this sets up an incentive system conducive to analysts providing

private information to their hedge fund clients. Anecdotally, the OAGNY (2014)

settlement states that BlackRock “directly rewarded participating analysts with higher

ratings in prominent financial industry magazine rankings” (OAGNY settlement, section

41, 2014).

Second, most hedge funds do not have their own trading desks (Brown,

Goetzmann, Liang, and Schwarz; 2008), instead, placing their trades elsewhere.

Goldstein, Irvine, Kandel, and Wiener (2009) find that institutions concentrate their order

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flow with a relatively small set of brokers, generating millions of dollars in trading fees

and commissions. Consequently, analysts may feel compelled to provide large hedge

funds with private information to maintain trading relations between fund and bank. For

example, the 2014 OAGNY settlement asserts that “given BlackRock’s position as huge

market participant, brokerage firms would respond to [BlackRock’s] requests where they

may otherwise have been reluctant to respond to retail or other small investors” (OAGNY

2014).

The rest of this paper is organized as follows. Section 2 discusses the information

leakage hypothesis and alternative explanations to finding abnormal trading prior to

recommendation changes. Section 3 describes our data sources, sample construction and

presents summary statistics. Sections 4 and 5 present the methodologies we employ and

the empirical results from our main and additional tests. Section 6 contains tests on the

reverse causality hypothesis. We summarize and offer some conclusions in Section 7.

2. Competing Hypotheses

2.1 Information Leakage Hypothesis

Our main hypothesis is that hedge funds learn about analysts’ stock

recommendations prior to their publication, and they subsequently profit from this

information by trading in these securities. We call this hypothesis the information

leakage hypothesis. Under this hypothesis, recommendation changes are private

information, and hedge funds profit from this private information by buying upgraded

stocks and selling downgraded stocks prior to the public release of these recommendation

changes.

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The motivation behind hedge funds obtaining private information about

subsequent stock recommendation changes is straightforward. If the public release of

recommendation changes is value relevant, then hedge funds could purchase stock in the

upgrade prior to the release of the analyst’s report or sell their holdings prior to public

release of the analyst’s downgrades.

The incentives behind research analysts transmitting directly or indirectly this

information to hedge funds is less obvious. We believe the change in the compensation

structure for analysts provided by the 2003 Global Settlement and codified by NASD

Rule 2711 and NYSE Rule 472 contributes to this transfer of information.

Before the Global Settlement, research (analysts) and investment banking

functions for 10 large investment banking houses were interrelated.4 Prior to the Global

Settlement, analysts’ compensation was based, in part, on investment banking revenues

or input from investment banking personnel (SEC, 2002a).5 The Global Settlement

ended this practice, with specific bans on investment bankers evaluating analysts and on

analysts’ compensation being based “directly or indirectly” on investment banking

revenues (SEC, 2002a). On May 10, 2002, the SEC approved NASD Rule 2711 and

NYSE Rule 472, which extended these bans to all investment banks.6

4 The 10 banks in the Global Settlement were Bear Stearns, Credit Suisse First Boston, Deutsche Bank,

Goldman Sachs, J.P. Morgan Chase, Lehman Brothers, Merrill Lynch, Morgan Stanley, Salomon Smith

Barney and UBS Warburg.

5 Michaely and Womack (1999) provide evidence that analysts from brokerage firms underwriting IPOs

issued more favorable recommendations on these IPOs than unaffiliated analysts. Grosberg, Healy, and

Maber (2011), using proprietary data from one “major investment bank,” link analyst compensation to

investment banking income generated by the analyst. However, their data span from 1988 to 2005, and

their analyses do not differentiate between pre-and-post Global Settlement periods.

6 Rule 2711(d) stated that “No member [investment bank] may pay any bonus, salary or other form of

compensation to a research analyst that is based upon a specific investment banking services transaction”

[my insertion]. Rule 472 stated that “no member or member organization may compensate an associated

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As a result, analysts’ compensation shifted from being responsive to its brokerage

house’s investment banking division to being responsive to its outside (sell-side)

clientele. In fact, on July 29, 2003, the SEC approved identical amendments to NASD

Rule 2711 and to NYSE Rule 472 that specifically codified this shift. In that amendment,

one of the factors that investment banks must use when determining their research

analysts’ compensation is “the overall ratings received from clients, [my emphasis] sales

force and peers independent of the member’s investment banking department, and other

independent rating services” (NASD Rule 2711 d(2)(C) and NYSE Rule 472 h(2)(iii)).

Thus, an analyst’s compensation was directly linked to how well he was perceived by his

clients relative to others analysts in the same industry.7

Consistent with our conjecture, Brown, Call, Clement, and Sharp (2013), in a

January 2013 survey of sell-side analysts, report that 67% of sell-side analysts cite their

standing in analyst rankings or broker votes as being “very important” to their

compensation; 64% of the analysts cite “accessibility and/or responsiveness” as an

important input into their compensation.8 Brown et al. (2013) also report that hedge

person(s) for specific investment banking services transactions. An associated person may not receive an

incentive or bonus that is based on a specific investment banking services transaction.”

7 On July 26, 2007, the Financial Industry Regulatory Authority (FINRA) was established. FINRA is the

successor to the NASD and the regulation, enforcement and arbitration arm of the NYSE. NASD Rule

2711 is now called FINRA Rule 2711. NYSE Rule 472 remains the same.

8 Beyer and Guttman (2010) discuss the link between analysts’ compensation and how analysts are ranked

by StarMine, a survey of several Wall Street firms that evaluates analysts. Grosberg et al. (2011) correlate

sell-side analyst compensation with whether the analyst is named as one of the top-three analysts or a

runner-up in the annual Institutional Investor survey of buy-side institution ratings. Their data span across

the 2003 time period delineating the NASD and NYSE rules. Further, in a face-to-face discussion with a

former analyst, we were told that his yearly bonus was greatly influenced by how his “team” performed on

two surveys - Overall Sector Research Rankings by Institutional Investor, Inc., and a confidential survey by

Greenwich Associates. Both contain rankings of different brokerage firms based on surveys of supply-side

analysts’ clients’ perception on how valuable and helpful the analysts were to them. This analyst also

provided us with a copy of each survey for the industry he was in. As an example of the importance of

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funds and mutual funds are the two most important clients that analysts have, with over

80% of surveyed analysts classifying them as being “very important,” compared to just

around 2% characterizing them as being “not important.” 9 More granularly, the New

York attorney general’s investigative report on its settlement with BlackRock’s use of

analysts’ surveys concludes that “BlackRock rewarded analysts who participated in

[these] surveys by assigning them higher ratings in industry rankings, which enhanced

the analysts’ careers, prominence and potentially their paychecks” (OAGNY, 2014).

Thus, it appears that analysts have a monetary incentive to be responsive to requests for

information from their hedge fund clientele.

Second, analysts may feel compelled to provide large hedge funds with private

information to maintain their other relations between fund and bank. Anecdotally, the

New York State’s attorney general’s settlement with BlackRock indicates that BlackRock

directs billions of dollars in securities trading annually through the world’s largest

financial institutions (OAGNY, 2014). Thus, supply-side analysts may have felt

pressured to give advance views on stocks to BlackRock to retain their business ties.

2.2 Alternative Hypothesis: Reverse Causality

One alternative explanation to the hypothesis that the flow of information goes

from the analyst to the hedge fund is that the information flow is in the opposite

direction. Under this reverse causality hypothesis, a stock’s aggregate trading

responsiveness, the Greenwich Associates survey ranks brokerage firms by how “intense” their service is,

and on the strength of the relationship between client and analyst.

9 In contrast, retail brokerage clients were cited as being “very important” to only 13% of the surveyed

analysts, with 52% of the analysts responding that these clients were “not important.”

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imbalance10

is public information and therefore is observable to analysts when they make

their recommendations. If a trading imbalance is a noisy signal for the private

information processed by informed investors, then an analyst observing specific trading

positions may rationally revise his/her recommendation. Therefore, any documented

abnormal trading volume by hedge funds prior to the recommendation change could be

consistent with analysts changing their recommendations after observing a high level of

trading imbalances.

There is some evidence that market participants pay attention to hedge fund

trading. Brown and Schwarz (2013) examine abnormal trading volume and abnormal

stock returns surrounding the filing of hedge funds’ Form 13F filings. They find higher

trading volume and positive returns for stocks with expanded positions on and up to two

days after the filing date.11

Brown and Schwarz (2013) and Brav, Jiang, Partnoy, and

Thomas (2008) also find excess trading volume in the week prior to hedge funds filing

their Form 13Fs and Form 13Ds, respectively. However, the determinants behind these

observations are unclear, as the excess volume could represent hedge fund buys or other

investors learning about the hedge funds’ positions.

2.3. Is it Profitable to Trade Before the Recommendation Date?

A necessary condition behind the information leakage hypothesis is that investors

can earn abnormal stock returns by trading prior to the release of recommendation

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We use the term trading imbalance to refer to whether an individual stock experienced net buys or net

sales during a trading day.

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Based on these findings, we calculated the filing delay between the quarter end date and the filing date

and removed from the sample the few companies that had a filing delay of 10 calendar days or less.

Consistent with Brown and Schwarz (2013), most firms filed within 40-45 days after quarter end, with a

large plurality filing exactly on the required 45 day filing delay.

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changes. Womack (1996), Jagadeesh, Kim, Krische, and Lee (2004), Asquith, Mikhail

and Au (2005) and Green (2006) present evidence that changes in analysts’ stock

recommendations contain new information, and, more germane to this study, elicit

significant abnormal stock reactions.

Because our sample period differs from the time frames used in these studies, we

re-do their analysis and calculate abnormal stock returns around our sample of stock

recommendation event dates. Following Daniel, Grinblatt, Titman, and Wermers (1997),

abnormal stock returns are estimated using 125 portfolios based on size, market-to-book

and momentum (see Appendix B).

Figure 1 contains the daily average abnormal stock returns around the public

announcement of analysts’ recommendation changes for our sample. As the figure

shows, there are little to no abnormal stock returns prior to day 0, the recommendation

change date. In contrast, on day 0, upgrades have an average abnormal stock return of

greater than 1% and downgrades have an average abnormal return of less than -3%.

These returns are consistent with prior research, for example, see Green (2006). Thus,

trading before the public release of a recommendation change can be a profitable

strategy.

3. Sample Construction and Descriptive Statistics

Our tests require data on institutional holdings, analysts’ stock recommendations,

and stock return data.

Our main hypothesis presumes a favored relation between the analyst and the

hedge fund. To maximize the probability that our sample of hedge funds are preferred

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clients, we include only hedge funds that have at least $10 billion under management for

at least two of the four years from 2006 through 2009. This filter yields a sample of 57

hedge funds. Similarly, we only include the analysts’ recommendations from the seven

large brokerage houses mentioned in Morgenson (2012). The sample period spans the

years mentioned by Morgenson (2012) and also is after the 2003 amendments to NASD

Rule 2711 and NYSE Rule 472.

We obtain quarterly institutional holdings from Thomson-Reuters Institutional

Holdings (13F) Database. Since 1978, all institutional investment managers (including

hedge funds, banks, insurance companies, and mutual funds) who exercise investment

discretion over accounts holdings of at least $100 million in securities are required by the

Securities Act Amendment of 1975 to make quarterly disclosures of portfolio holdings to

the SEC on a Form 13F within 45 days of the quarter end. 12

For reporting purposes, the

ending date is the trading date of the security and not the settlement date. Form 13F

reporting items include security type, security issuer, cusip number, number of shares,

and the market value of each security owned.

We hand-check our list of 57 hedge funds with each Form 13F’s investor’s name.

Some of the hedge funds also have mutual funds, and we remove those holdings from the

Form 13F filings so as to examine hedge fund trading only. We define Δsharej,t-1 as the

change in the hedge fund’s dollar holding in stock j between the end and beginning of

quarter t-1.

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Managers may request confidential treatment to delay public disclosure of some or all of the holdings

reported on Form 13F. Agarwal, Jiang, and Yang (2010) find that hedge funds frequently obtain investment

confidentiality, thereby resulting in significant delays of their holdings. See, also Goldstein (2014) on

Greenlight Capital asking the SEC for a seven-day delay in disclosing its molding in Micron Technology.

To the extent that manager “opt out” of 13F disclosures, our data understates the potential flow of

information between analysts and hedge funds

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We also examine trading patterns for other large institutions, specifically for

banks, insurance companies, and growth-oriented mutual funds that traded at least once

in firms followed by a FirstCall analyst during our sample period of 2005 to 2011. We

identify banks and insurance companies by the Thomson-Reuters manager type codes in

the Thomson-Reuters Institutional Holdings (13F) Database. We classify institutions

within the “s 12” files in the Thomson-Reuters Database as mutual funds. To make the

mutual funds comparable to the hedge funds, we include only mutual funds that list their

investment objectives in Thomson-Reuters as aggressive growth, growth or growth and

income. For all groups, we aggregate funds by specific manager levels. Our control

sample consists of 146 banks, 27 insurance companies, and 319 mutual funds,

respectively.

Table 1 presents summary statistics of institutional holdings and quarterly

changes in holdings over our time period. As Panel A shows, quarterly individual stock

holdings vary from $28.38 million to $98.55 million per quarter. Hedge funds and

mutual funds tend to have the largest average individual stock holdings, with banks and

insurance companies holding lower dollar amounts of individual stocks. Panels B and C

present average quarterly net purchases and net sales of individual stocks. In Panel B, we

see that a hedge fund’s average purchase of any stock is $10.95 million; mutual funds,

banks and insurance companies’ individual stock purchases average $9.68, $5.24 and

$7.44 million respectively. In Panel C, the average sale of an individual stock is $19.95

million for hedge funds, and $9.83, $10.56 and $17.35 million for mutual funds, banks

and insurance companies, respectively. While the dollar trading purchases or sales are

statistically different from each other across institutions, we argue that they are

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economically comparable. We also note that institutions engage in many purchases and

sales per quarter. Using the full Thomson-Reuters database, we find an average of

130,000 transactions per quarter over the time period (untabulated). We therefore

conclude that all four institutions can be characterized as active stock traders.

Analysts’ recommendations are from the FirstCall database. Analyst

recommendations in FirstCall lie on a scale of one through five, with one corresponding

to a strong buy and five indicating a strong sell. We reverse the scaling (e.g., five is a

strong buy and one is a strong sell) to allow an upgrade to be a positive number and a

downgrade to be a negative number.

We use the FirstCall database instead of the I/B/E/S database for two key reasons.

First, FirstCall updates its recommendations in real time. I/B/E/S, on the other hand,

updates its recommendations on a weekly or monthly basis. Therefore, the published time

on FirstCall is more accurate. A number of prior studies using the FirstCall database

when timing is important include Green (2006), Barber, Lehavy, McNichols, and

Trueman (2007), and Christophe et al. (2012). Second, Ljungqvist, Malloy, and Marston

(2009) document widespread changes to the historical I/B/E/S analyst stock

recommendations database, including alterations of recommendations and additions and

deletions of records. Barber, Lehavy, and Trueman (2010) find no such inconsistencies in

the FirstCall database. Therefore using the FirstCall database may provide more accurate

recommendation data.

A potential disadvantage of using FirstCall recommendations relative to I/B/E/S

recommendations is lower coverage by FirstCall. According to WRDs,13

FirstCall

13

A copy of the WRDS document can be obtained from

http://drcwww.uvt.nl/its/voorlichting/handleidingen/datastream/IBESonWRDS.pdf

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collects data from 451 brokerage houses, compared to more than 2,700 brokerage houses

covered by I/B/E/S. FirstCall contains 15,714 companies whereas I/B/E/S contains nearly

70,000 firms. To see the impact that this lower coverage has on our data collection, we

compare the FirstCall database with the I/B/E/S database for our sample of stock

recommendations. Appendix C contains our findings.14

In terms of coverage, we have

123,953 total stock recommendations. This compares to 126,988 for the I/B/E/S database.

We attribute the large number of FirstCall recommendations to the fact that we include

seven major brokerage houses only. In terms of overlaps between databases, we find

consistent data coverage on reported dates and, more specifically, on recommendations

matched by date. We therefore conclude that FirstCall and I/B/E/S are similar for our

group of brokerage firms.

Table 2 contains summary statistics on analysts’ stock recommendations. As

Panel A illustrates, there are 74,548 recommendations for the seven brokerage firms in

our sample. Of these stock recommendations, 46% are upgrades, 33% are downgrades,

and the remaining 21% are no changes. As Panel B shows, we capture a total of 58,541

recommendation changes on 4,439 unique firms. From Panel C, we observe that

approximately 47% of our recommendations are one level changes in recommendation.

We also examine the quarterly distribution of the recommendation changes (untabulated)

over the 2005-2011 time period. With the exception of three quarters, 2006(Q3),

2007(Q3), and 2008(Q2), recommendation changes are evenly distributed across time,

with an average of around 2,000 recommendations changes per quarter.

14

We present the data across the seven brokerage firms we use in the study. We identify these firms by

number (1 through 7) to preserve anonymity.

19

Stock return data are from CRSP, and accounting data are from Compustat. All

variables are winsorized at the extreme 1%.

4. Trading Before the Recommendation Date: Methodology and Empirical Results

We begin our analyses by testing for an increase in hedge fund trading prior to the

public release of an analyst upgrade or downgrade. Instead of looking at aggregate

trading imbalances in any stock (e.g., Christophe et al., 2010), we examine individual

hedge fund trades prior to recommendation changes. We predict that hedge funds buy

prior to upgrades and sell prior to downgrades.

4.1 Methodology

Ideally, we would like to have daily trading data for our hedge funds. However,

hedge funds are not required to disclose their trades or holdings unless (1) they reach a

threshold of being a beneficiary owner of 5% of a firm’s equity or (2) the hedge fund

holds at least $100 million of total equity. In the first instance, hedge funds are required

to file a Form 13D or 13G within 10 days of reaching the 5% threshold. In the second

case, hedge funds are required to file a Form 13F within 45 days after quarter end March

31, June 30, September 30, and December 31.

We use Form 13F data to determine hedge fund holdings, as well as changes in

hedge fund holdings. Form 13F contains a breakdown of the hedge fund’s holdings by

each stock – both the number of shares and the value of these shares are disclosed. Thus,

we are able to determine whether a hedge fund’s holding in a particular stock increased

or decreased over the reported quarter.

20

We want to examine hedge fund trading prior to recommendation changes. To do

this, we line up all recommendation changes by trading days following the end date of a

Form 13F filing. Figure 2 illustrates this process. Suppose, for example, that the Form

13F end date is March 31. If a recommendation change is issued on April 1 (assume it is

a trading day), then we call that Day +1; if the recommendation change is issued on April

2 (again assume it is a trading day), then we call that Day +2; and so forth. Similarly, if

the Form 13F end date is June 30, then Day +1 would be a recommendation change

issued one trading day after June 30, Day +2 would be a recommendation issued two

trading days after June 30; and so forth. Thus, all of the stock recommendations are

issued subsequent to the trading activity of the hedge funds.

We assign each stock recommendation change to a portfolio based on the number

of days between the Form 13F quarter-end date and the trading day of the

recommendation ( ). We run the following regression individually for each

portfolio:

Δsharej,t-1 = αi + βi Δrecj(Dayi) + FEt-1, for i = [+1, +2, …+10] (1),

where Δsharej,t-1 is the dollar value change in stock j over quarter t-1 and Δrecj(Dayi) is an

indicator representing a recommendation change on stock j for Day i. The indicator

variable takes on the value of 1 for an upgrade, and a value of -1 for a downgrade. If the

analyst issues a “no change” recommendation on Day i or if there is no recommendation

issued on Day i, the value of Δrecj(Dayi) takes on the value of zero. FEt-1 is a fixed effects

variable for the year of quarter t-1. Since Δsharej,t-1 is based on institutional holdings at

the 13F reporting date at the end of quarter t-1, and all recommendation changes occurs

21

after this 13F report date, we ensure that all changes in holdings occur prior to

recommendation changes.

This identification strategy allows us to infer if and on what trading day hedge

funds trade prior to the recommendation issuance date. To illustrate, assume hedge funds

systematically trade three days prior to the public announcement of an analyst’s

recommendation change. If a recommendation change for stock j occurs on day +3, then

hedge funds would trade systematically on stock j on day 0, which is the last trading date

of quarter t-1. Since Δsharej,t-1 contains that change in holdings for stock j in quarter t-1,

β3 will capture the association between the pre-recommendation change trading in quarter

t-1 and the recommendation change issued on day +3. If the recommendation change

occurs on day +2, then hedge funds would trade on day -1, i.e., the trading day prior to

the end of quarter t-1. Since Δsharej,t-1 also contains that change in trading, β2 will

capture the association between trading in stock j in quarter t-1 and the recommendation

change issued on day +2. If the recommendation change occurs on day +1, then hedge

funds would trade systematically on day -2, which is two trading days prior to the end of

quarter t-1. Since Δsharej,t-1 contains that change in trading, β1 will capture the hedge

funds’ pre-recommendation change trades associated with that issuance date.

Conversely, the coefficient β4 will not capture pre-recommendation trading because that

trade would occur on day +1, which would not be reflected in Δsharej,t-1, since that trade

occurs after the close of quarter t-1. As this illustration shows, if hedge funds

systematically trade more than three days prior to the recommendation change day, then

that pre-recommendation trading would not be captured by any βi coefficient greater than

22

β3. Note too that we would need to find significantly positive coefficients on β1, β2 and

β3, inclusive, to infer a three-day pre-recommendation trading window.

We acknowledge that our identification scheme imposes a data limitation. We

seek to address this issue by limiting the subset of recommendation changes to those

issued up to 10 trading days after the Form 13F quarter-end date (i.e., [Dayi = +1,

+2,…+10]).15

The rationale for this decision is that if hedge funds trade on private

information, then we expect the timing of these trades to be relatively close to the

recommendation change date (e.g., see Irvine et al. 2007; Kadan et al., 2013). Therefore,

by restricting our recommendations to those occurring a few days subsequent to the Form

13F end date, we believe the changes in quarterly holdings for these stocks most likely

capture the trading activity related to any transfer of private information. Table 2, Panel

D contains a summary of the number of recommendation changes by upgrade,

downgrade and no change for Days +1 through +10. As the panel shows,

recommendations are evenly distributed over the 10-day period. In addition,

comparisons with Panel A show that the percentages of upgrades, downgrades, and no

change recommendations for the 10-day period are similar to those percentages for the

full sample.

Table 2, Panel E contains a summary of the total number of observations by

upgrade, downgrade and no changes for days +1 through +10 that we use when

estimating equation (1). The number of observations encompasses all hedge fund trades

associated with recommendation changes made on the issuance date. A comparison of

Panel E with Panel D shows that, on average, three of the 57 hedge funds trade on any

15

Expanding the trading days to 20 days after the 13F quarter end produces the same inferences as those

presented in the paper.

23

recommendation change. This suggests that a small number of hedge funds trade prior to

the recommendation change, a finding consistent with a preferred analyst/client

relationship. We examine this hypothesis further in section 5.2.

We include yearly fixed effects to remove the average change in holdings due to

macroeconomic events. Because we use pooled data, hedge fund holdings, and the

brokerage firm may appear more than once. Therefore, we cluster our standard errors by

hedge fund and brokerage firm (Thompson, 2011). To ensure that our results are not

influenced by other recommendations changes, we remove all recommendations that are

accompanied by other recommendations in the prior 14 days.16

We also eliminate any

recommendation change issued five days after an earnings announcements or a

management forecast.

4.2 Empirical Results

4.2.1 Hedge Funds and Future Recommendation Changes

Table 3, Panel A presents summary statistics for regression (1) for our sample of

hedge funds. In column (1), we examine changes in hedge fund holdings preceding both

upgrades and downgrades. Consistent with the information leakage hypothesis, we find

significantly positive βi coefficients for days +1 and +2. The coefficients for day +1 and

day +2 are 2.23 (p<0.01) and 4.37 (p<0.10), respectively. Thus, we can infer that hedge

funds trade one and two days prior to a recommendation change. These results, coupled

16

We also examine the sample of recommendation changes in Panel D to see if there is a clustering

(simultaneity) of brokerage house recommendation changes on any calendar date. We find that the 11,518

recommendations for days +1 through +10 are comprised of 11,462 distinct company-calendar day

observations. Of these 11,462 company-calendar day observations, 11,407 (99.5%) are lone

recommendations, 54 company-calendar day observations (0.5%) are by two different brokerage houses,

and only one company-calendar day observation is by three different brokerage houses, the latter being

three “no change” recommendations. We conclude that there is minimal clustering of brokerage house

recommendations for the same stock on the same calendar date.

24

with the insignificant coefficients on days +3 through +10, are consistent with hedge

funds, on average, not trading in the direction of recommendation change until two days

prior to a recommendation change.17

We also extend the period to 20 days [untabulated]

and find insignificant coefficients for days +11 through +20 for this and all specifications

in Table 3.18

In columns (2) and (3), we examine upgrades and downgrades separately. For

upgrades, only the coefficient on day +1 is significantly different from zero. On average,

hedge funds buy $2.03 million of each upgraded firm’s stock relative to a firm without an

upgrade. For downgraded firms, the coefficients on days +1 and +2 are significantly

different from zero.19

Thus, hedge funds sell an average of $12.23 million ($2.51 million

+ $9.72 million) of holdings for each downgraded firm relative to a firm without a

downgrade. Our results imply that hedge funds tend to trade earlier for downgrades (up to

two days prior) than for upgrades (one day prior).

The results in Panel A also suggest that pre-trading is unlikely to be driven by

hedge fund forecasting ability. Pre-trading occurs up to two days prior to the public

release of the recommendation change. If hedge funds were able to forecast these

changes, we would expect to see increased trading across the ten-day window, as they

cannot forecast the precise timing of the subsequent upgrade or downgrade. The two-day

17

In addition to the insignificant t-statistics on the beta coefficients for days +3 through +10, we note that

many of the coefficients on these days are also negative, a sign inconsistent with hedge funds buying prior

to upgrades and selling prior to downgrades.

18

As an alternative, we benchmark upgrades and/or downgrades against the sample of no change

recommendations disclosures only. The results, untabulated, are consistent with those shown in Table 3.

19

Since the variable takes on a -1 value for downgrades, a positive coefficient is consistent with the hedge

fund reducing its holdings in the quarter prior to day i.

25

window, on the other hand, is consistent with hedge funds having advanced notice of the

date of public release of recommendation changes.

4.2.2 Other Large Institutional Traders and Future Recommendation Changes

We next examine if other large institutional traders display similar pre-

recommendation change trading patterns as hedge funds. Table 3 Panels B-D present

regression results for changes in share holdings in quarter t-1 for recommendation

changes in days +1 through +10 for banks, insurance companies, and growth-oriented

mutual funds.20

Recall that a positive β coefficient is consistent with the institution either

increasing its holdings prior to an upgrade or decreasing its holdings prior to a

downgrade.

When combining both upgrades and downgrades together, we find no βi

coefficient significantly different from zero at standard levels. We particularly note that,

unlike Panel A, the βi coefficients on days +1 and +2 are insignificantly different from

zero.

For upgrades, we find no pattern of significant βi coefficients that is consistent

with other institutional traders systematically buying the stock of a firm prior to the

analysts’ public release of an upgrade. For banks, only the β3 coefficient is significantly

different from zero, albeit of the wrong sign. For insurance companies and mutual funds,

only one βi coefficient is significantly different from zero for each group (for insurance

20

Before conducting our analysis, we examine the trading patterns of our samples of hedge funds, banks,

and insurance companies to see if the control firms have comparable trading panels to the treatment firms

for the ten recommendation days that we use in equation (1). We find that all three institutions trade

frequently in the stocks prior to the recommendation changes, with hedge funds and banks having similar

number of trades per quarter throughout the test period. We therefore conclude that any differences in

trading patterns between institutions will not be attributable to trading frequencies.

26

companies, β6 = 0.61, p < 0.01; for mutual funds, β5 = 3.38, p < 0.05). However, given

our identification strategy, we would need to see a pattern of significantly positive

sequential β coefficients – for example, for insurance companies β1 through β6 would all

have to be significantly positive for us to infer that insurance companies are trading up to

six days prior to the upgrade announcement.

Similarly, we find no pattern of significant βi coefficients that is consistent with

other institutions systematically selling the stock of a firm prior to an analyst’s public

release of a downgrade. For banks and mutual funds, none of the βi coefficients are

significantly different from zero. The significantly negative β8 coefficient for insurance

companies is inconsistent with insurance companies selling prior to a downgrade.

The results in this section suggest that the pre-recommendation trading we

observe for hedge funds is not representative of trading patterns for other large

institutional investors. These findings may explain the inconsistent pre-recommendation

trading results found by Kadan et al. (2013), who examine differences in trading patterns

between all institutional investors and individuals.

5. Additional Tests

5.1 Trade Reversals: Recommendations vs. Other Holdings

In this section, we examine trade reversals. Our main hypothesis is that investors

who take advantage of a “recommendation change premium” will act like transient

investors (e.g., Bushee, 1998; 2001). This implies they will buy or sell securities in the

direction of the upcoming recommendation changes and reverse their holdings shortly

27

afterwards. In contrast, trades in quarter t-1 driven by expected fundamental values of the

underlying firm will be more permanent.

We classify a particular holding in stock j as a purchase in quarter t-1 if the hedge

fund increased its holdings in that stock, i.e., Δsharej,t-1 > 0. For each hedge fund, we

create two portfolios of stocks: all stock purchases in quarter t-1 with subsequent

upgrades (Upgrades); and all other stock purchases – downgrades excluded – (Other

Holdings). Our test is to compare the average percent changes in holdings in quarter t for

each group across all hedge funds. The percent change in holdings for stock j is defined

as the dollar change in shareholdings in stock j in quarter t divided by the total dollar

holdings in stock j at the end of time t-1. Based on Table 3’s results, Upgrades include

only those firms with an upgrade in day +1 or +2. We propose a difference in the

unwinding of the purchases between Upgrades and Other Holdings, with hedge funds

selling the upgrade group more quickly than their other investments.

We undertake a similar procedure to analyze downgrades. We classify a

particular holding as a sale in quarter t-1 if the hedge fund reduced its holdings in that

stock, i.e., Δsharej,t-1 < 0. For each hedge fund, we create two portfolios: all sales in

quarter t-1 with downgrades within 2 days of quarter end t (Downgrades) and all sales in

other holdings – upgrades excluded – (Other Holdings). We propose a difference in the

repurchases in quarter t between the two groups, with hedge funds being more likely to

repurchase downgrades vis-à-vis net sales in their other holdings.

Table 4 contains the results for our trading reversal analysis. Panel A presents the

mean percent changes in security holdings in quarter t following a purchase of that

security in quarter t-1. Hedge funds sell, on average, 16% of their holdings in Upgrades

28

in the quarter of the upgrade (quarter t). In contrast, they purchase an additional 15% in

quarter t for their other holdings. The difference of -31% between the two groups is

statistically significant at less than the 0.01 level. Median values [untabulated] yield

similar interpretations. Thus, we conclude that trading patterns for hedge funds differ by

whether they buy stocks prior to recommendation changes or for other reasons.

Specifically, the empirical findings are consistent with hedge funds reversing their

holdings for the upgraded group of stocks after the recommendation is made public, but

increasing their holdings for other net purchases made in quarter t-1.

Panel A also presents the same analysis for banks, insurance companies, and

mutual funds. We find no differences in trading patterns for any of these institutions by

whether the purchase of the security is preceded by an analyst upgrade or not. We

believe these results complement our interpretation that hedge funds trade

opportunistically prior to upgrades. In particular, we note that growth-oriented mutual

funds act as value investors as they buy more stock in quarter t for stocks upgraded in

that quarter.

In Panel B, we present mean changes in security holdings in quarter t following a

sale of a security in quarter t-1. Hedge funds repurchase, on average, 24% of

downgraded stocks in quarter t, but sell off, on average, 90% of their remaining holdings

in quarter t for their other net sales in quarter t-1. The difference between the two groups

is statistically significant (t-stat = 40.17). Median trading patterns [untabulated] in

quarter t yield similar interpretations. In contrast, we find no differences in trading

patterns in quarter t between Downgrades and Other Holdings for banks, insurance

companies or mutual funds. We therefore conclude that trading patterns for hedge funds

29

differ in quarter t by whether they sell securities prior to an analyst downgrade in the first

two days of quarter t or for other reasons.

Panels C and D further examine the subsequent trading reversals by hedge funds

following the disclosure of a recommendation change. In Panel C, we present the

percentage of hedge funds that sell all (100%), half (50%) or some percentage of their

holdings after purchasing the security in quarter t-1. As the panel shows, 21% of stocks

purchased in quarter t-1 prior to an upgrade are sold off completely in quarter t; this

compares to 0% of stocks purchased in quarter t for other reasons. Similarly, 28% of

stocks purchased in quarter t-1 prior to an upgrade are at least half sold off in quarter t,

compared to just 13% of stocks purchased for other reasons. Panel D shows repurchases

in quarter t for stocks that had net sales in quarter t-1. Comparisons between trading

patterns between Downgrades and Other Holdings show differences between the two

groups. Thirteen percent of stocks with net sales in quarter t-1 prior to downgrades have

at least 100% of the shares repurchased in quarter t. In contrast, only 2% of net sales in

the Other Holdings group result in a full repurchase in quarter t. Similarly, hedge funds

repurchase some stock back 41% of the times for Downgrades, but only 5% for Other

Holdings.

In summary, the trading reversals documented in Table 4 are consistent with

hedge funds being transient investors for purchases or sales preceding recommendation

changes, but not for purchases or sales related to other reasons. Trading reversals are

consistent with an information leakage hypothesis in that they suggest hedge funds buy

(sell) on upcoming information but sell (buy back) the stock shortly after the

recommendation disclosure. The lack of trading reversals for banks, insurance

30

companies and mutual funds lend further support for the information leakage hypothesis

for hedge funds only.

5.2 Favored Hedge Fund-Brokerage House Relationships

In this section, we test for a favored relationship between one hedge fund and a

few brokerage houses. Goldstein, et al. (2009) find that institutions concentrate their

order flow with a relatively small set of brokers. These order flows generate millions of

dollars for the brokerage firms, thus providing an incentive for analysts to cooperate with

large, actively trading hedge funds (OAGNY, 2014). Accordingly, we predict that a

hedge fund-brokerage house relation for any large hedge fund would be limited to a few

brokerage firms.

We begin by creating the variable, Trade Ratioj, for each stock j held by an

individual hedge fund. Trade Ratioj is equal to Δsharej,t-1 divided by the average Δsharet-1

for all stocks in the hedge fund’s portfolio at the end of quarter t-1. Note that Trade

Ratioj is anchored around 1.0, as its value is relative to the average change in holdings for

all stocks owned by the hedge fund at the end of quarter t-1. To capture large trades, we

keep only those values that are greater than 1.0.

For each individual hedge fund, we place each Trade Ratioj into one of seven

silos, based on the identity of the brokerage house that published the recommendation

change. We then rank the silos from high to low, with the highest silo indicating the

brokerage firm eliciting the greatest dollar trading prior to its recommendation changes,

the next highest silo being the brokerage house whose recommendations result in the next

highest amount of dollar trading, and so forth.

31

We repeat this process for each of the 57 hedge funds in our sample and then

average each silo across hedge funds. We thus have created 7 new variables, Rank 1 (the

highest), Rank 2, … Rank 7 (the lowest). Each variable contains the Average Trade Ratio

across funds by the high-to-low rank order of the relative dollar value trades prior to a

brokerage firm’s recommendation change. If individual hedge funds receive the flow of

information from many brokerage firms, then there should be no difference in the

Average Trade Ratios across ranks. On the other hand, if individual hedge funds receive

the flow of information from a few brokerage houses, then the Average Trade Ratios of

the higher ranked variables (e.g., Rank 1, Rank 2) will be greater than the lower ranked

variables. We refer to the latter possibility as a favored relationship between brokerage

house and an individual hedge fund.

Table 5, Panel A presents the results. We find evidence consistent with the

favored relationship hypothesis. The average hedge fund investment in stocks preceding

recommendation changes issued by the most (second most) related broker is 6.90 (3.48)

times the average investment in that quarter. In contrast, the Average Trade Ratio issued

by other brokers ranges from 2.90 for Rank 3 to 2.07 for Rank 7. The p-value under each

rank is a test for the difference between Rank x and Rank x+1. For example under Rank

1, the p-value represents a test on whether Rank 1 is different from Rank 2. (Since we

end with Rank 7, there is no test for Rank 7, hence no p-value.) Our results indicate that

each hedge fund tends to invest heavily in stocks with future recommendation changes

from only two related brokers. We interpret these results as evidence that each hedge

fund obtains information about future recommendation changes from one or two

brokerage houses only.

32

To see if any of the seven individual brokerage house are more likely to be

associated with a leakage of information, we calculate an Average Trade Ratio across

recommendation day trades for each brokerage firm. We then test whether each broker’s

dollar trade value is different from all of the other six brokers. The results are shown in

Panel B. With the exception of Broker 5, the Average Trade Ratio is insignificantly

different from each other. These results are consistent with Morgenson’s (2012)

assertion that all of the brokerage houses provide information to their large hedge fund

clients.

5.3 Abnormal Stock Returns: Recommendation Changes vs. Other Holdings

In this section, we compare the hedge funds’ abnormal returns in quarter t by

whether the net changes in holdings in quarter t-1 are related to recommendation changes

in days +1 or +2 or to other reasons. This test holds the stock picking ability of a hedge

fund constant because the only difference between the two groups are the underlying

stocks held by the hedge fund. Thus, finding a significant difference in abnormal returns

would allow us to rule out a forecasting ability hypothesis on the part of the hedge fund

as an alternative hypothesis to the information leakage hypothesis. That is, if there is

something special about the hedge fund’s stock picking ability, then there is no reason

why this “special” skill would apply only to stocks of firms for which there are

subsequent recommendation changes.

We do the analysis separately for upgrades and for downgrades. In both cases,

the treatment portfolio consists of stocks held by hedge funds in quarter t-1 with stock

recommendation changes on days +1 or +2. For upgrades, we create a portfolio called

33

Net Purchase Upgrades, which consists of all stocks upgraded on days +1 or +2 in which

the aggregate net change in dollar investment in an individual stock j is positive in

quarter t-1. That is, we require a net increase in the dollar investment in stock j across all

hedge funds. The control portfolio, called Other Net Purchases, contains all other stocks

owned by the hedge funds with increases in the aggregate net change in dollar

investments in quarter t-1. Thus, for both portfolios, the aggregate holdings for each

included stock increased over quarter t-1. For downgrades, we do the same sorting for

Net Sale Downgrades and for Other Net Sales. Net Sale Downgrades is the portfolio of

downgraded stocks on Days +1 or +2 with reductions in each stock’s aggregate net

investment over quarter t-1. Other Net Sales are stocks without these downgrades that, in

the aggregate, had a net decrease in dollar investment over quarter t-1. Thus, for both

portfolios, the aggregate holdings for each included stock decreased over quarter t-1. We

value-weight each stock in the respective portfolio by dividing the change in net

investment in stock j in quarter t-1 with the change in net investment for all stocks in the

portfolio.21

Each portfolio is recalculated at the end of every quarter t-1, based on the

latest fund trades and is held for the next quarter. Using this methodology, we effectively

mimic an aggregated hedge fund trading dynamic for the recommendation change

subgroup.

To account for variations in return expectation models, we calculate monthly

alphas for quarter t based on the Fama-French four-factor model (with momentum), the

Fama-French three-factor model, and a CAPM model. Table 6 presents the alphas and

21 For example, suppose there are only two hedge funds in our sample and the aggregate net investment in

all stocks is $1000. If hedge fund A purchased $100 of stock j and hedge fund B sold $50 of stock j, the net

investment in stock j would be $50, and the weight used for stock j would be 0.05 ($50/$1000).

34

their t-statistics for the treatment and control portfolios, with Panel A examining net

purchases and Panel B examining net sales. The table also contains the coefficients on

the Fama-French four factors and their t-statistics to allow for comparisons among

portfolios.

In Panel A, the four-factor return model’s monthly alpha for Net Purchase

Upgrades is 0.83% (t-stat = 2.75). This translates into a quarterly abnormal return of

2.49%, and a yearly abnormal return of 9.96%. The three-factor and CAPM monthly

alphas are 0.83% (t-stat = 2.42) and 0.89% (t-stat = 2.53), respectively. In contrast,

hedge fund purchases in equities that do not undergo a recommendation change in days

+1 or +2 (Other Net Purchases) earn a four-factor monthly alpha of -0.00% (t-stat = -

0.67). Three-factor and CAPM monthly alphas are similarly insignificantly different from

zero. Loadings on the Fama-French factors are comparable across the two portfolios.

Panel B presents the monthly alphas for the portfolios of stocks in which the

hedge funds sold shares in quarter t-1. By selling equities prior to a downgrade, hedge

funds avoid a monthly four-factor alpha of -0.94% (t-stat = -2.46). In contrast, stocks

sold for other reasons in quarter t-1 (Other Net Sales) avoid an insignificantly monthly

alpha of 0.15% (t-stat = 0.63). We report similar alphas for the three-factor Fama-French

and the CAPM models. Loadings on the Fama-French factors appear to be comparable

across the two portfolios.

In summary, the abnormal returns for the upgrade/downgrade portfolios are

consistent with the information leakage hypothesis as it relates to upcoming analysts’

revisions in their recommendations. Hedge funds earn significantly positive alphas in

quarter t by purchasing stocks prior to upgrades, and they avoid significant losses in

35

quarter t by selling stocks prior to downgrades. In addition, the insignificant alphas in

quarter t for the net purchases/net sales in quarter t-1 for reasons other than anticipating

future recommendation changes are consistent with the view that hedge funds do not

have superior forecasting abilities in selecting stocks.

6. Tests on Reverse Causality

Under the reverse causality hypothesis, the flow of information goes from the

hedge fund to the analyst and not the other way around. In Section 4, we document an

increase in trading activity prior to an analyst recommendation change, with abnormal

buys preceding upgrades and abnormal sells preceding downgrades. We interpret these

findings as evidence of analysts providing private information to hedge fund traders.

However, a stock’s aggregate trading imbalance is public information and

therefore is observable to analysts when they make their recommendations. If a trading

imbalance is a noisy signal for the private information processed by informed investors,

then an analyst observing a particular trading imbalance may rationally revise his/her

recommendation. Therefore, the documented abnormal trading volume by hedge funds

prior to the recommendation change could be evidence that analysts change their

recommendations after observing a high level of trading imbalances.

We test this possibility with three tests connecting pre-recommendation trading to

subsequent analysts’ recommendation changes. In the first test, we examine the link

between abnormal trading volume by all market participants prior to a recommendation

change and the probability that a subsequent analyst recommendation change occurs.

36

Specifically, for our sample of hedge fund traded stocks, we estimate the following

regression:

Abnormal Volumej,t = αj + βj Δrecj,0 + εj,t (2),

where Abnormal Volumej,t is the trading volume by all market participants for stock j on

day t divided by its average trading volume for trading days -40 through -10 (see

DeFond, Hung, and Trezeant 2007), and Δrecj,0 is the recommendation change disclosed

by one of the seven brokerage firms in our sample for stock j on day 0; as before, -1

represents a downgrade and 1 represents an upgrade.

We estimate regression (2) individually for each day t, where t goes from -5

through +5. To reduce the effects of noise trading, we benchmark the upgrades or

downgrades against analysts’ issuances of recommendations that re-affirm the previous

recommendation (i.e., a no change recommendation group). Trading volume is from

CRSP and since it is aggregate market trading volume, we can calculate abnormal trading

volume for five days before and after recommendations changes. Abnormal Volume are

winsorized at the 1% cut-off.

Panel A of Table 7 shows the daily regression coefficients and t-statistics for

Upgrades vis-à-vis No Changes for Days [-5, +5]. As the panel shows, the only

coefficient that is statistically different from zero is on Day 0, the day of the upgrade. In

contrast, we find no significant coefficient for days [-5, -1], and most germane to our

previous results, the coefficients on days -1 and -2 are insignificantly different from zero.

Thus, we discern no abnormal trading activity prior to the announcement date, or put

differently, we find no evidence suggesting that analysts issue an upgrade on stock j after

observing abnormal trading volume on that stock. Panel B presents the daily coefficients

37

and t-statistics for Downgrades vis-à-vis No Changes. Again, the only statistically

significant coefficient is for Day 0, the day of the downgrade. The coefficients on days [-

5, -1] are insignificantly different from zero, a finding inconsistent with analysts’ issuing

downgrades after observing abnormal trading volume.

Our next two tests examine the connection between pre-recommendation trading

by hedge funds and subsequent analysts’ recommendation changes. First, we identify all

large hedge fund trades in quarter t-1 and calculate the number of times these large trades

are followed by analyst recommendation changes in the first three trading days of quarter

t. To ensure that we are picking truly large trades, we take only the top 1% of all dollar

buys and the top 1% of all sales in each quarter t-1.

Table 8 contains these results. We classify 2,396 trades as large hedge fund

purchases. Of these trades, only 41 (1.71%) subsequent upgrades are within three trading

days after the Form 13F quarter t-1 end date. To calibrate whether this is a significantly

large number, we randomly select 2,396 observations (with replacement) for days [+4,

+30] and calculate the percent of upgrades over this time period. We repeat this process

1,000 times, thereby creating a bootstrapped distribution. We create a similar

bootstrapped distribution for days [+1, +3]. We then test for the difference in means

between the two distributions. As the last row in Table 8 shows, the t-statistic is 0.64,

which is insignificantly different from zero at conventional levels. Thus, we find no

evidence that analysts’ upgrades in days [+1, +3] are preceded by large hedge fund

purchases, a result inconsistent with the reversed flow of information hypothesis.

Similarly, we classify 1,850 trades as large hedge fund sells. Of these trades, only

23 (1.24%) are followed by downgrades within three trading days of the Form 13F

38

quarter t-1 end date. Using the same bootstrapping method, but with 1,850 observations

(with replacement), we find that the t-statistic testing for differences between the

percentage in downgrades in days [+1, +3] and days [+4, +30] is 0.71. This too suggests

that analysts’ downgrades in days [+1,+3] are not preceded by large hedge fund sells in

the prior quarter.

Our last test is a mirror image of Table 5. In Table 5, we examine how many

times a recommendation change from a particular broker is preceded by a large trade by a

specific hedge fund. In this section, we examine how often a brokerage firm’s

recommendation change follows a specific hedge fund’s large trade. That is, we propose

that if brokerage houses are recalibrating their recommendations based on observed

hedge fund trading, then, for each brokerage firm, the flow of information should be

observed from a limited number of hedge funds. The results, untabulated, do not support

this conjecture. We find no difference in the percentage of recommendation changes in

days +1 through +10 by the identity of the large hedge fund trader in quarter t-1. Our

results hold for all recommendation changes, for upgrades and for downgrades.

In summary, we present evidence against a reversed causality flow of information

from hedge fund trading to recommendation changes.

7. Summary and Conclusions

This paper examines information flow between large hedge funds and stock

recommendation changes from sell-side analysts of large brokerage firms. We present

four main results. First, we find evidence that hedge funds trade ahead in the direction of

stock recommendation changes. Second, these pre-recommendation trades are more

temporary than the hedge fund’s other holdings. Third, pre-recommendation trades are

39

concentrated within a small group of related brokers. Lastly, hedge funds earn abnormal

stock returns only when they are trading on this private information. Taken together these

results provide evidence in favor of an information leakage hypothesis.

We also compare hedge fund trading patterns to other institutional traders –

namely banks, insurance companies and large growth-oriented mutual funds. We find no

evidence supporting the view that these other financial institutions trade prior to analysts’

recommendation changes. Nor do we see differences in holding horizons between stocks

purchased (sold) prior to upgrades (downgrades) and their other net purchases or sales.

Thus, the hedge fund trading patterns that we observe prior to analysts’ recommendation

changes do not hold for other institutional traders.

As an alternative explanation for our pre-recommendation trading results, we

explore the possibility that the flow of information goes from the hedge fund (through its

trading activity) to the analyst (who revises his/her recommendation based on this

information). Our empirical tests are inconsistent with this reverse causality explanation,

thus buttressing our conclusion that hedge funds appear to trade on private, selective

disclosures by sell-side analysts.

Finally, we add a caveat to the interpretation of our findings. Since daily or intra-

daily trades by hedge funds are not publicly available, our research design relies on

aligning recommendation changes close to quarterly-end dates from hedge fund Form

13F filings. Thus, we are unable to conclusively determine the exact date of the hedge

fund trades. Nevertheless, our results, in total, can be interpreted as being consistent with

a transfer of information from analysts to hedge funds prior to the recommendation

changes.

40

Appendix A

Legal and Regulatory Environment22

Unlike Regulation FD, which specifically prohibits the selective disclosure by issuers

(firms) of material nonpublic information, there are no federal, legal restrictions prohibiting the

selective disclosure by analysts of material nonpublic information (MNPI). However, brokerage

houses may have internal policies proscribing the transfer of material non-public information both

within and outside the firm. For example, in the Massachusetts Securities Division Consent

Order with Citigroup, Section I contains excerpts from Citigroup’s policy concerning “restrictions

on the use of material non-public information.” Citigroup’s written policy strictly prohibits its

employees from providing information to anyone who does not have a need to know, “even if the

employee doesn’t believe that the person will act on the information” (MSD 2013, section 104).

Further, Citigroup’s written policy on “confidential and material nonpublic information” provides

examples of information likely to be considered material, including changes in research

recommendations (MSD 2013, section 105).

Trading on or supplying material confidential information for the purpose of trading may

be construed as a violation of federal inside trading or securities laws, although we note that we

are unaware of any SEC or Department of Justice case brought against a hedge fund or a

brokerage on selectively disclosing forthcoming stock recommendation changes. In the MSD

consent decree, section 111 alleges that Citigroup violated the 1934 Exchange Act to “establish,

maintain and enforce reasonable policies and procedures to prevent the misuse of material,

nonpublic information.” The decree also alleges violation of FINRA and NASD rules and

regulations designed to “prevent the improper disclosure of confidential nonpublic information”

(MSD 2013, section 110).

22

This appendix is based on the Massachusetts and New York settlements, SEC documents, and informal

discussions with attorneys from FINRA, the New York Attorney General’s office, and Wachtell Lipton.

All interpretations are the authors’.

41

States also have securities laws. The Citigroup consent decree alleged violations by

Citigroup of the Massachusetts Uniform Securities Act, and the BlackRock Settlement with the

New York Attorney General’s Office alleged violations by BlackRock of the Martin Act. Both

state Acts are broadly based. Neither imposes scienter (intention) on the violator, establishing

instead standards of strict liability, “unethical behavior,” or not observing “high standards of

commercial honor” as violations of the state’s security laws. The Martin Act was used

extensively by Attorney General Elliot Spitzer against Wall Street firms. For example, its use was

instrumental in creating the 2003 Global Analysts Settlement. The Martin Act gives the New

York Attorney’s General wide latitude in gathering information, issuing subpoenas, reaching

settlements, and imposing sanctions. Further, a violation of the Martin Act can result in a

misdemeanor, which can result in imprisonment of the violator up to a year in state prison.

42

Appendix B

Portfolio selection procedure abstract from Daniel et al. 1997

“This appendix discusses the formation of the 125 size, book-to-market, and momentum

sorted benchmark characteristic portfolios. Beginning in July 2005, and in each following

July, we place every common stock listed on NYSE, AMEX, and Nasdaq into portfolios,

provided these firms meet our data requirements. Our criteria for inclusion are similar to

those spelled out in Fama and French (1993). We require that COMPUSTAT data be

available for at least two years prior to the inclusion of the firm in the sample, and that

the firm have market value data available on CRSP at the end of December and the end

of June preceding the formation date. In addition, we require that the firm have at least

six monthly returns available on CRSP in the 12 months preceding the formation date

(for the momentum calculation). The portfolios are all value-weighted, buy-and-hold

portfolios.

The composition of each of the 125 portfolios is based on a triple-sort on each firm's

market equity value (or size), book-to-market ratio, and momentum. Each formation date,

the universe of common stocks is first sorted into quintiles based on each firm's market

equity just prior to the formation date (i.e., on the last day of June).

The breakpoints for this sort are based on NYSE firms only, although NYSE, AMEX,

and Nasdaq stocks are included in the analysis. Then, the firms within each size quintile

are further sorted into quintiles based on their book-to-market ratio. The book-to-market

ratio is the ratio of the book-value at the end of the firm's fiscal year during the calendar

year preceding the formation date to the market value at the end of the preceding

December. Here, we "industry adjust" the book-to-market ratios by subtracting the long-

term industry average book-to-market ratio from each individual firm's ratio, following

Cohen and Polk (1995). We define 50 industries depending on the underlying firm's

principal Standard Industrial Classification (SIC) code as reported by CRSP.

Finally, the firms in each of the 25 size/BM portfolios are then sorted into quintiles based

on their preceding twelve-month return, giving us a total of 125 portfolios.”

(Daniel et al. 1997)

(Irvine et al. 2007REf et al. 2009 et al. 2010), (Barber et al. 2006), (Go

43

Appendix C

I/B/E/S vs. FirstCall

This table compares the number of observations for all recommendations by the seven brokers used in this study from the I/B/E/S and FirstCall databases. Column (3) reports

the percent of observations that have the same reported date in both databases. Column (4) reports the percent of observations that have the same recommendation between

the two databases.

Brokerage Firm

by Number Number of observations Matched observations

(1)

I/B/E/S

(2)

FirstCall

(3) Reported date

matched

(4) Recommendation matched conditional

on date matched

1 13,517 21,503 94.67% 86.70%

2 13,760 20,491 74.02% 90.02%

3 11,939 12,599 62.92% 94.08%

4 38,166 25,607 60.88% 86.29%

5 34471 16, 576 63.04% 70.01%

6 15,135 27,177 96.63% 91.86%

7 0 31, 152 0 0%

Total Number of

Recommendations 126,988

123,953

75.36%

86.49%

44

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47

Figure 1

Distribution of Abnormal Returns Surrounding Analyst Recommendations

In the figure below, the average buy and hold abnormal return surrounding analyst recommendations are shown. Only

recommendations unaccompanied by any other recommendation within the prior 14 days are included. Day 0 is the date of the

recommendation as shown in the FirstCall database. Recommendations are ranked from 1 through 5, with 1 being a “strong sell” and

5 being a “strong buy.” Upgrade is an increase in the ranking, downgrade is a decrease in the ranking and no change is a zero change

in the ranking. The estimation period is from 2005 through 2011. Following (Daniel et al. 1997), abnormal returns are estimated using

125 portfolios based on size, market to book and momentum. See Appendix B for a description of Daniel et al. (1997)

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

-60-56-52-48-44-40-36-32-28-24-20-16-12 -8 -4 0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 Upgrades

Downgrades

No change

48

Figure 2

Depiction of Relation Between Form 13F End Date and Stock Recommendation Change Trading Day

49

Table 1

Institutional Holdings Summary Statistics

This table shows the average quarterly holdings in individual stocks (Panel A), quarterly net

purchases of individual stocks (Panel B), and quarterly net sales of individual stocks (Panel C) for

different institutions. Only hedge funds with over $10 billion of assets under management

between 2006 and 2009 are included. Only banks, insurance firms and growth-oriented mutual

funds that have invested in at least one of the FirstCall analyst-followed firms between 2005 and

2011 are included.

Panel A: Average Holdings in Each Stock ($ Mil)

Hedge Funds

(N=57) Banks (N=146)

Insurance Firms

(N=27)

Mutual Funds

(N=319)

2005 62.40 30.55 35.95 57.02

2006 64.03 35.80 47.49 83.16

2007 68.24 44.99 67.64 98.55

2008 58.66 43.84 59.55 93.74

2009 50.17 28.38 44.11 57.04

2010 33.06 33.90 34.97 64.50

2011 37.99 40.32 43.06 68.16

Mean $53.51 $36.83 $47.54 $76.52

Panel B: Average Quarterly Net Purchases of Individual Stocks ($ Mil)

Hedge Funds

(N=57) Banks (N=146)

Insurance Firms

(N=27)

Mutual Funds

(N=319)

2005 11.90 4.50 5.10 8.25

2006 9.37 2.56 5.04 11.05

2007 14.83 10.61 12.27 11.90

2008 12.46 5.09 10.12 11.42

2009 8.58 3.46 7.57 9.50

2010 9.13 6.07 5.51 6.77

2011 10.40 4.42 6.49 6.63

Mean $10.95 $5.24 $7.44 $9.68

Panel C: Average Quarterly Net Sales of Individual Stocks ($ Mil)

Hedge Funds

(N=57) Banks (N=146)

Insurance Firms

(N=27)

Mutual Funds

(N=319)

2005 -12.87 -4.05 -6.88 -9.43

2006 -47.08 -31.67 -41.41 -10.52

2007 -34.67 -19.61 -28.21 -11.72

2008 -13.67 -4.48 -11.47 -12.48

2009 -11.09 -3.60 -12.99 -7.34

2010 -9.66 -6.41 -9.56 -6.56

2011 -10.64 -4.12 -10.94 -8.62

Mean -$19.95 -$10.56 -$17.35 -$9.83

50

Table 2

Recommendation Changes Summary Statistics

This table shows the distribution of analyst recommendations from the FirstCall database. Only

recommendations coded as “real time updated” are included. Only recommendations from the

seven brokers in Morgenson (2012) are included. Recommendations are coded as: 1-StrongSell 2-

Sell 3-Neutral 4-Buy 5-Strong Buy.

Panel A- Recommendation Classifications

Upgrades Downgrades No Changes Total

Number 34,034 24,507 16,007 74,548

Percent 46% 33% 21% 100%

Panel B- Firms

Total number of recommendation changes 58,541

Total number of unique firms 4,439

Number of times each firm appears in sample Number of firms

1 to 10 2,568

11 to 20 864

21 to 30 500

31 to 40 304

41 to 49 122

50> 81

Panel C- Recommendation Change Frequencies Change in

Recommendations Frequency Percent

Cumulative

Frequency

Cumulative

Percent

-4 185 0.25 185 0.25%

-3 438 0.59 623 0.84

-2 8,257 11.08 8,880 11.92

-1 15,627 20.96 24,507 32.88

0 16,007 21.47 40,514 54.35

1 19,209 25.77 59,723 80.12

2 14,248 19.11 73,971 99.23

3 406 0.54 74,377 99.77

4 171 0.23 74,548 100%

51

Table 2 – continued

Panel D: Number of Recommendation Classifications by Trading Day in Quarter t

Number of

Trading Days

After Form 13F

Quarter End

All

Recommendations Upgrades Downgrades No Change

+1 1,214 537 438 239

+2 1,134 494 410 230

+3 1,103 507 371 225

+4 1,128 466 410 252

+5 1,071 486 370 215

+6 1,237 551 470 216

+7 1,262 572 414 276

+8 1,228 587 404 237

+9 1,192 529 409 254

+10 949 421 334 194

Total 11,518 5,150 4,030 2,338

Percent of All

Recommendations 44.7% 35.0% 20.3%

Panel E: Number of Observations by Trading Day in Quarter t Number of

Trading Days

After Form 13F

Quarter End

All Observations Upgrades Downgrades No Change

+1 3,679 1,690 1,324 665

+2 3,741 1,524 1,604 613

+3 3,100 1,226 1,222 652

+4 2,533 1,240 766 527

+5 3,541 1,633 1,175 733

+6 3,557 1,523 1,192 842

+7 3,710 1,793 1,397 520

+8 3,926 1,790 1,476 660

+9 3,043 1,369 1,103 571

+10 3,051 1,288 1,007 756

Total 33,881 15,076 12,266 6,539

Percentage of All

Observations 44.50% 36.20% 19.30%

52

Table 3

Stock Trading Prior to Recommendation Changes

This table presents summary statistics for regressions on changes in stock holdings in quarter t-1

on subsequent recommendation changes. In Panel A, only hedge funds with over $10 billion of

assets under management between 2006 and 2009 are included in the regression. In Panel B, we

present results with banks; in Panel C, we present results with insurance companies; and in Panel

D, we present results with large growth mutual funds (mutual funds with over $10 billion assets

and with a stated growth objective). The estimated regression is:

Δsharej,t-1 = αi + βi Δrecj(Day i) + FEt-1,

where Δsharej,t-1 is the difference between ending and beginning holdings for stock j in quarter t-

1, ( )is the change in the analyst’s recommendation for stock j in quarter t,; -1 represents

a downgrade and 1 represents an upgrade. Δrecj(Day i) is 0 for all other trades. We run the above

regression individually for each subgroup, where i goes from +1 through +10. Each

represents the number of trading days the recommendation change is issued after the Form

13F reporting date. Annual fixed effects are included and standard errors are two-way clustered

by hedge fund and brokerage firm. Observations falling on the extreme 1% are winsorized. The

estimation period is from 2005 to 2011. The number of observations for each Dayi in Panel A is

in Table 2, Panel E. The number of observations for Panels B through D depends on the holdings

of the institution in quarter t-1. *** significant at 1% level; ** significant at 5% level; *

significant at 10% level.

Panel A: Hedge Funds

Upgrades and

Downgrades

(1)

Upgrades Only

(2)

Downgrades Only

(3)

Trading day of

recommendation

change after

Form 13F end

date

T-stat T-stat T-stat

, i=1 2.23*** 2.66 2.03** 2.13 2.51* 1.65 , i=2 4.37* 1.83 -1.19 -0.50 9.72** 2.37 , i=3 -1.88 -1.09 -3.50 -1.15 -0.14 -0.11 , i=4 -0.75 -0.47 -1.44 -0.69 0.39 0.14 , i=5 0.02 0.03 0.45 0.20 -0.46 -0.29 , i=6 0.37 0.67 -0.21 -0.21 0.97 0.74 , i=7 0.08 0.12 0.86 0.64 -0.89 -0.52 , i=8 -0.69 -1.48 -0.84 -0.30 -0.39 -0.67 , i=9 -1.53 -0.52 -0.05 -0.01 -3.42 -0.43 , i=10 -0.97 -0.53 -2.40 -0.79 0.97 0.60

53

Table 3 - continued

Panel B: Banks

Upgrades and

Downgrades

(1)

Upgrades Only

(2)

Downgrades Only

(3)

Trading day of

recommendation

change after

Form 13F end

date

T-stat T-stat T-stat

, i=1 0.31 0.75 1.01 0.39 0.20 0.20

, i=2 -0.07 -0.06 3.26 1.02 -3.97 -2.30

, i=3 -0.37 -1.14 -3.40* -1.75 0.72 0.58

, i=4 -0.41 -0.54 0.08 0.95 -1.20 -1.12

, i=5 0.10 0.29 1.09 0.87 -0.64 -0.52

, i=6 0.09 0.47 -0.11 -0.20 0.20 0.28

, i=7 -0.16 -1.05 0.12 0.49 -0.42 -0.79

, i=8 0.09 1.12 0.12 0.62 0.01 0.04

, i=9 0.56 0.58 1.77 0.51 0.35 0.08

, i=10 0.62 1.43 2.62 1.38 -0.33 -0.31

Panel C: Insurance Companies

Upgrades and

Downgrades

(1)

Upgrades Only

(2)

Downgrades Only

(3)

Trading day of

recommendation

change after

Form 13 end

date

T-stat T-stat T-stat

, i=1 -0.70 -0.73 0.04 0.07 -1.05 -1.17

, i=2 0.28 0.29 0.79 0.52 -0.94 -0.42

, i=3 -0.68 -1.12 -2.32 -1.40 0.67 0.84

, i=4 -0.69 -1.17 -2.39 -1.01 -0.08 -0.07

, i=5 -2.14 -1.33 -4.10 -1.32 -3.14 -1.15

, i=6 -0.16 -0.43 0.61*** 3.55 -1.10 -1.00

, i=7 0.18 0.6 -1.01 -0.56 1.17 1.43

, i=8 -0.04 -0.11 0.15 0.30 -0.75*** -3.59

, i=9 -0.14 -0.11 -4.39 -1.34 4.52 1.52

, i=10 -0.40 -0.52 4.00 1.09 -5.92 -1.10

54

Table 3 – continued

Panel D: Growth-Oriented Mutual Funds

Upgrades and

Downgrades

(1)

Upgrades Only

(2)

Downgrades Only

(3)

Trading day of

recommendation

change after

Form 13F end

date

T-stat T-stat T-stat

, i=1 -0.10 -0.27 0.76 0.56 -0.95 -1.08

, i=2 0.15 0.29 1.00 0.92 -1.35 -1.07

, i=3 -0.04 -0.06 -0.52 -0.38 -1.78 -1.16

, i=4 -0.06 -0.16 1.15 1.04 -1.17 -1.22

, i=5 0.32 0.95 3.38*** 2.05 -1.79 -1.50

, i=6 -0.07 -0.17 0.70 0.67 -0.69 -0.98

, i=7 0.23 0.71 -0.28 -0.20 1.15 0.81

, i=8 0.04 0.37 0.15 0.42 -0.08 -0.30

, i=9 -0.12 -0.24 0.18 0.12 -0.83 -0.72

, i=10 0.10 0.19 0.77 0.74 -0.21 -0.16

55

Table 4

Trading Reversals Subsequent to Recommendation Changes

This table shows the average percent change in holdings of stocks in quarter t. Panel A presents mean

values for institutions that increased their holdings in a stock in quarter t-1. The institution’s portfolio of

stocks in quarter t-1 is divided into those stocks with an upgrade in Days [+1,+2] and those stocks without

an upgrade in those days – downgrades excluded (Other Holdings). Panel B presents mean values for

institutions that decreased their holdings in a stock in quarter t-1. The institution’s portfolio of stocks in

quarter t-1 is divided into those stocks with a downgrade in Days (+1,+2) and those stocks without a

downgrade in those days – upgrades excluded (Other Holdings). Panel C shows the percentage of hedge

funds with trade reversals (net sales) in quarter t with net purchases in quarter t-1. Panel D shows the

percentage of hedge funds with trade reversals (net purchases) in quarter t with net sales in quarter t-1.

Hedge funds include only those with over $10 billion of assets under management between 2006 and 2009.

Only banks, insurance firms and mutual funds that have invested in at least one stock followed by a

FirstCall analyst between 2005 and 2011 are included. Observations falling on the extreme 1% are

winsorized. ***

is significant at the 0.01 level.

Panel A- Institution Increased its Holdings in Stock j in Quarter t-1 (Purchases)

Mean Change in Holdings in Quarter t

Upgrades Other Holdings Difference

T-statistic for Difference

between Upgrades and

Other Holdings

Hedge funds -0.16 0.15 -0.31 -13.34 ***

Banks -0.62 -0.63 0.01 -0.42

Insurance firms 0.18 0.18 0.00 0.05

Mutual Funds 0.49 0.44 0.04 0.28

Panel B- Institution Decreased its Holdings in Stock j in Quarter t-1 (Sales)

Mean Change in Holdings in Quarter t

Downgrades Other Holdings Difference

T-statistic for Difference

Between Downgrades and

Other Holdings

Hedge funds 0.24 -0.90 1.14 40.17 ***

Banks -0.73 -0.71 -0.03 -0.72

Insurance firms 0.11 0.14 -0.03 -0.77

Mutual Funds 0.21 0.19 0.02 0.07

56

Table 4 - continued

Panel C: Hedge Fund Increased its Holdings in Stock j in Quarter t-1

(Net Purchases)

% of total subsample

Percentage of quarter t-1 holdings sold

in quarter t Upgrades

Other

Holdings

100% 21% 0%

> 50% 28% 13%

> 0% 59% 44%

Panel D: Hedge Fund Decreased its Holdings in Stock j in Quarter t-1

(Net Sales)

% of total subsample

Percentage of quarter t -1 holdings

repurchased in quarter t Downgrades

Other

Holdings

>100% 13% 2%

> 50% 18% 2%

> 0% 41% 5%

57

Table 5

Special Relationship between Hedge Fund-Brokerage House

This table examines whether hedge funds tend to trade prior to recommendation changes made by a

few or by a large number of the seven brokerage firms used in this study. Only trades relating to

recommendation changes in days +1 and +2 are included. includes trades

that are larger than the hedge fund's average dollar value trading volume in the same quarter.

Therefore a value of 2 implies that the dollar value trade is twice the hedge fund's average trading

amount in that quarter. Panel A shows the rank order in which hedge funds trade on a brokerage

firm’s recommendation change, with Rank 1 being the highest dollar value of trades and Rank 7

being the smallest dollar value of trades. The p-value measures if Rank x is different from Rank

x+1. Panel B contains the Average Trade Ratio across the seven brokerage firms. The p-value

measures if Broker x is different from all of the other Brokers.

Panel A- Within an Individual Hedge Fund

Rank 1 Rank 2 Rank 3 Rank 4 Rank 5 Rank 6 Rank 7

Average Trade Ratio 6.90** 3.48** 2.90 2.67 2.64 2.57 2.07

p-value 0.01 0.03 0.54 0.93 0.79 0.16

Panel B- Across Brokerage Firms

Broker 1 Broker 2 Broker 3 Broker 4 Broker 5 Broker 6 Broker 7

Average Trade Ratio 4.99 3.14 3.56 4.32 5.83** 3.28 2.81

p-value 0.18 0.30 0.55 0.67 0.03 0.26 0.18

58

Table 6

Abnormal Stock Returns

This table shows hedge funds abnormal stock returns and t-statistics for quarter t. Only hedge funds with over $10 billion of managed assets between 2006 and

2009 are included. The estimation period is from 2005 through 2011. Firms that experience an upgrade in days +1 or +2 are classified as upgrade firms. Firms

that experience a downgrade in days 1 and 2 are classified as downgrade firms. If there is an increase in the stock holdings in quarter t-1, we classify that as a net

purchase. If there is a decrease in holding within a particular stock, we classify that as a net sale. Only recommendations unaccompanied by another

recommendation within the last 14 days are included. The four-factor alpha is from the Fama-French 3-factor plus momentum model. The three-factor model is

from the Fama-French 3-factor model. The CAPM alpha is from a CAPM model. MKT, SMB, HML, and UMD are the monthly market return minus the

monthly risk-free interest rate, the small-minus-large risk factor, the high-minus-low book-to-market ratio, and momentum factor respectively, all available on

Ken French’s website. Panel A compares a hedge fund’s upgrades with its other net purchases in quarter t-1. Panel B compares a hedge fund’s downgrades with

its other net sales in quarter t-1. *,**

,***

significant at the 0.10, 0.05, and 0.01 levels, respectively.

Panel A- Net Buys in Quarter t-1

Four-Factor Alpha

(monthly)

Three-Factor Alpha

(monthly) CAPM Alpha MKT SMB HML UMD

Net Purchase

Upgrades 0.0083

*** 0.0083

** 0.0089

** 1.15

*** 0.45

*** -0.14 -0.29

***

t-statistic 2.75 2.42 2.53 14.73 3.15 -1.11 -4.75

Other Net Purchases -0.0000 -0.0000 -0.0000 1.10*** 0.29* 0.16 -0.47

***

t-statistic -0.67 -0.54 -0.45 12.03 1.77 1.09 -6.71

Panel B- Net Sales in Quarter t-1

Four-Factor Alpha

(monthly)

Three-Factor Alpha

(monthly) CAPM Alpha MKT SMB HML UMD

Net Sale Downgrades -0.0094**

-0.0094***

-0.01 1.29***

0.39**

0.43***

-0.08

t-statistic -2.46 -2.46 -2.16 12.90 2.15 2.65 -1.02

Other Net Sales 0.0015 0.0015 0.0019 0.93***

0.28**

-0.26***

-0.33***

t-statistic 0.63 0.05 0.62 15.27 2.54 -2.60 -6.91

59

Table 7

Reverse Causality Test 1

Do Analysts’ Recommendation Changes Follow Abnormal Trading Volume?

This table presents summary statistics for the regression of abnormal trading volume on analysts’

recommendations. Panel A presents the statistics for upgrades vis-à-vis no changes in

recommendation. Panel B presents the statistics for downgrades vis-à-vis no changes in

recommendation. For stocks our sample of hedge fund traded in, we estimate the following

regression:

Abnormal Volumej,t = αj + βj Δrecj,0 + εj,t

where Abnormal Volumej,t is the trading volume by all market participants for stock j on day t

divided by its average trading volume for trading days -40 through -10, and Δrecj,0 is the

recommendation change disclosed by one of the seven broker in our sample for stock j on day 0;

-1 represents a downgrade; 0 represents no change; and +1 represents an upgrade. We run the

above regression individually for each Day t subgroup, where t goes from -5 through +5.

Abnormal Volume observations falling on the extreme 1% are winsorized. The estimation period

is from 2005 to 2011. *** significant at 1% level; ** significant at 5% level; * significant at 10%

level.

Panel A: Upgrades vs. No Changes in Recommendation

Trading Day Coefficient T-statistic

-5 -0.020 -0.43

-4 0.055 1.07

-3 0.052 1.00

-2 0.017 0.43

-1 0.030 0.91

0 0.315 2.88***

1 0.046 0.57

2 0.108 1.55

3 -0.024 -0.43

4 0.034 0.65

5 0.038 0.69

60

Table 7 – continued

Panel B: Downgrades vs. No Changes in Recommendation

Trading Day Coefficient T-statistic

-5 -0.040 -0.67

-4 0.001 0.01

-3 0.024 0.45

-2 0.054 1.12

-1 0.001 0.02

0 0.383 3.03***

1 0.085 0.86

2 0.059 0.78

3 0.032 0.38

4 0.062 1.05

5 0.049 0.74

61

Table 8

Reverse Causality Test 2

Are Analysts’ Recommendation Changes in Quarter t Related to Large Hedge Fund Trades

in Quarter t-1?

This table shows the number and percentage of times that a large hedge fund net purchase (net

sale) in quarter t-1 is followed by an analyst upgrade (downgrade) within the first three days of

quarter t. A large hedge fund net purchase (net sale) is classified as the top 1% of all hedge fund

net purchases (net sales) in quarter t-1. The distributions for the t-statistic testing for the

difference between the percentage of upgrades (downgrades) in days [+1, +3] and days [+4,

+30] are generated by bootstrapping 1,000 random samples for each time period. *** significant

at 1% level; ** significant at 5% level; * significant at 10% level.

Large Hedge Fund

Net Purchases in

Quarter t-1

Large Hedge

Fund Net Sales

in Quarter t-1

Number of Large Net

Purchases

2,396 Number of Large

Net Sales

1,850

Number (percent) of Large

Net Purchases Followed

by Upgrades in Days [+1,

+3]

41

(1.71%)

Number (percent) of

Large Net Sales

Followed by

Downgrades in Days

[+1, +3]

23

(1.24%)

Percent of Large Net

Purchases Followed by

Upgrades in Days [+4,

+30]

1.69% Percent of Large Net

Sales Followed by

Downgrades in Days

[+4, +30]

1.13%

T-statistic that Percentage

for Days [+1,+3] is

different than Percentage

for Days [+4, +30]

0.64 T-statistic that

Percentage for Days

[+1,+3] is different

than Percentage for

Days [+4, +30]

0.71